Overview

Dataset statistics

Number of variables21
Number of observations13540
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory168.0 B

Variable types

NUM17
CAT2
BOOL2

Reproduction

Analysis started2020-08-03 01:30:10.144114
Analysis finished2020-08-03 01:30:51.837425
Duration41.69 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

symbol is highly correlated with df_indexHigh correlation
df_index is highly correlated with symbolHigh correlation
nbb is highly correlated with lastprice and 2 other fieldsHigh correlation
lastprice is highly correlated with nbb and 2 other fieldsHigh correlation
nbo is highly correlated with lastprice and 2 other fieldsHigh correlation
currentreferenceprice is highly correlated with lastprice and 2 other fieldsHigh correlation
week is highly correlated with quarter and 1 other fieldsHigh correlation
quarter is highly correlated with week and 1 other fieldsHigh correlation
month is highly correlated with quarter and 1 other fieldsHigh correlation
year is highly correlated with quarterHigh correlation
quarter is highly correlated with yearHigh correlation
matched_to_unmatched is highly skewed (γ1 = 44.00783743) Skewed
pct_move_snapshot_to_close is highly skewed (γ1 = -38.47594513) Skewed
df_index has unique values Unique
nearindicativeprice has 3622 (26.8%) zeros Zeros
farindicativeprice has 7729 (57.1%) zeros Zeros
pct_move_snapshot_to_close has 532 (3.9%) zeros Zeros

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count13540
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6824.6439438700145
Minimum0
Maximum13653
Zeros1
Zeros (%)< 0.1%
Memory size105.8 KiB
2020-08-02T18:30:51.905206image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile679.95
Q13409.75
median6825.5
Q310238.25
95-th percentile12972.05
Maximum13653
Range13653
Interquartile range (IQR)6828.5

Descriptive statistics

Standard deviation3942.111345
Coefficient of variation (CV)0.5776288664
Kurtosis-1.199112826
Mean6824.643944
Median Absolute Deviation (MAD)3414.5
Skewness0.0005596608777
Sum92405679
Variance15540241.86
2020-08-02T18:30:52.029349image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
20471< 0.1%
 
26601< 0.1%
 
47591< 0.1%
 
68061< 0.1%
 
6611< 0.1%
 
27081< 0.1%
 
129471< 0.1%
 
88491< 0.1%
 
108961< 0.1%
 
47431< 0.1%
 
Other values (13530)1353099.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
136531< 0.1%
 
136521< 0.1%
 
136511< 0.1%
 
136501< 0.1%
 
136491< 0.1%
 

symbol
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count993
Unique (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean493.1884047267356
Minimum0
Maximum992
Zeros14
Zeros (%)0.1%
Memory size105.8 KiB
2020-08-02T18:30:52.145017image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile48
Q1245
median492
Q3741
95-th percentile942
Maximum992
Range992
Interquartile range (IQR)496

Descriptive statistics

Standard deviation286.4688027
Coefficient of variation (CV)0.5808506445
Kurtosis-1.19821321
Mean493.1884047
Median Absolute Deviation (MAD)248
Skewness0.009155092402
Sum6677771
Variance82064.37494
2020-08-02T18:30:52.240424image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
991140.1%
 
826140.1%
 
946140.1%
 
930140.1%
 
922140.1%
 
914140.1%
 
906140.1%
 
898140.1%
 
882140.1%
 
874140.1%
 
Other values (983)1340099.0%
 
ValueCountFrequency (%) 
0140.1%
 
1140.1%
 
2140.1%
 
3140.1%
 
4140.1%
 
ValueCountFrequency (%) 
992140.1%
 
991140.1%
 
990140.1%
 
989140.1%
 
988140.1%
 

currentvolume
Real number (ℝ≥0)

Distinct count13508
Unique (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2644811.52127031
Minimum18984
Maximum132238624
Zeros0
Zeros (%)0.0%
Memory size105.8 KiB
2020-08-02T18:30:52.349120image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum18984
5-th percentile149586.95
Q1418231
median964846
Q32348006
95-th percentile9754039.65
Maximum132238624
Range132219640
Interquartile range (IQR)1929775

Descriptive statistics

Standard deviation6189570.522
Coefficient of variation (CV)2.340269041
Kurtosis102.3919017
Mean2644811.521
Median Absolute Deviation (MAD)679318
Skewness8.187461622
Sum3.5810748e+10
Variance3.831078325e+13
2020-08-02T18:30:52.448777image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4345682< 0.1%
 
3958792< 0.1%
 
6607352< 0.1%
 
13088472< 0.1%
 
3912632< 0.1%
 
2062272< 0.1%
 
8721352< 0.1%
 
1761722< 0.1%
 
2537662< 0.1%
 
2203542< 0.1%
 
Other values (13498)1352099.9%
 
ValueCountFrequency (%) 
189841< 0.1%
 
194151< 0.1%
 
195051< 0.1%
 
263671< 0.1%
 
272331< 0.1%
 
ValueCountFrequency (%) 
1322386241< 0.1%
 
1283050991< 0.1%
 
1275725711< 0.1%
 
1265816271< 0.1%
 
1037039881< 0.1%
 

lastprice
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count10184
Unique (%)75.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.67545466765141
Minimum1.425
Maximum1226.1
Zeros0
Zeros (%)0.0%
Memory size105.8 KiB
2020-08-02T18:30:52.553779image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1.425
5-th percentile7.51925
Q125.12165
median49.715
Q391.49
95-th percentile216.374
Maximum1226.1
Range1224.675
Interquartile range (IQR)66.36835

Descriptive statistics

Standard deviation88.53948806
Coefficient of variation (CV)1.185657167
Kurtosis39.75592151
Mean74.67545467
Median Absolute Deviation (MAD)29.9279
Skewness4.788344496
Sum1011105.656
Variance7839.240946
2020-08-02T18:30:52.646427image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
61.816< 0.1%
 
246< 0.1%
 
16.66< 0.1%
 
29.356< 0.1%
 
55.036< 0.1%
 
8.256< 0.1%
 
18.685< 0.1%
 
38.295< 0.1%
 
47.325< 0.1%
 
185< 0.1%
 
Other values (10174)1348499.6%
 
ValueCountFrequency (%) 
1.4251< 0.1%
 
1.431< 0.1%
 
1.4751< 0.1%
 
1.481< 0.1%
 
1.522< 0.1%
 
ValueCountFrequency (%) 
1226.11< 0.1%
 
1225.711< 0.1%
 
1178.061< 0.1%
 
1177.81< 0.1%
 
1163.7151< 0.1%
 

nbb
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count8776
Unique (%)64.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.63900443131462
Minimum1.42
Maximum1225.71
Zeros0
Zeros (%)0.0%
Memory size105.8 KiB
2020-08-02T18:30:52.752114image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1.42
5-th percentile7.5095
Q125.12
median49.685
Q391.48
95-th percentile216.234
Maximum1225.71
Range1224.29
Interquartile range (IQR)66.36

Descriptive statistics

Standard deviation88.45694252
Coefficient of variation (CV)1.185130257
Kurtosis39.71749354
Mean74.63900443
Median Absolute Deviation (MAD)29.925
Skewness4.784211578
Sum1010612.12
Variance7824.630679
2020-08-02T18:30:52.874408image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
36.2280.1%
 
40.8870.1%
 
3.2870.1%
 
38.2270.1%
 
16.670.1%
 
13.6970.1%
 
25.876< 0.1%
 
51.66< 0.1%
 
18.686< 0.1%
 
18.136< 0.1%
 
Other values (8766)1347399.5%
 
ValueCountFrequency (%) 
1.422< 0.1%
 
1.472< 0.1%
 
1.522< 0.1%
 
1.772< 0.1%
 
1.851< 0.1%
 
ValueCountFrequency (%) 
1225.711< 0.1%
 
1225.31< 0.1%
 
1178.431< 0.1%
 
1177.721< 0.1%
 
1162.861< 0.1%
 

nbo
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count8815
Unique (%)65.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.70780132939439
Minimum1.43
Maximum1226.87
Zeros0
Zeros (%)0.0%
Memory size105.8 KiB
2020-08-02T18:30:52.999345image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1.43
5-th percentile7.5195
Q125.13
median49.72
Q391.555
95-th percentile216.3675
Maximum1226.87
Range1225.44
Interquartile range (IQR)66.425

Descriptive statistics

Standard deviation88.61379971
Coefficient of variation (CV)1.186138504
Kurtosis39.88743264
Mean74.70780133
Median Absolute Deviation (MAD)29.935
Skewness4.795638201
Sum1011543.63
Variance7852.405499
2020-08-02T18:30:53.093214image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
21.7370.1%
 
10.370.1%
 
28.8370.1%
 
3.2970.1%
 
4.6970.1%
 
7.096< 0.1%
 
7.356< 0.1%
 
13.76< 0.1%
 
38.156< 0.1%
 
5.86< 0.1%
 
Other values (8805)1347599.5%
 
ValueCountFrequency (%) 
1.432< 0.1%
 
1.482< 0.1%
 
1.532< 0.1%
 
1.782< 0.1%
 
1.861< 0.1%
 
ValueCountFrequency (%) 
1226.871< 0.1%
 
1226.741< 0.1%
 
1179.81< 0.1%
 
1178.991< 0.1%
 
1164.571< 0.1%
 

imbside
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size105.8 KiB
0
6890
1
6650
ValueCountFrequency (%) 
0689050.9%
 
1665049.1%
 

imbalancesize
Real number (ℝ≥0)

Distinct count12293
Unique (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182328.3599704579
Minimum1
Maximum15632131
Zeros0
Zeros (%)0.0%
Memory size105.8 KiB
2020-08-02T18:30:53.200367image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile514.85
Q16496.5
median34317
Q3131598.75
95-th percentile778643.85
Maximum15632131
Range15632130
Interquartile range (IQR)125102.25

Descriptive statistics

Standard deviation592706.488
Coefficient of variation (CV)3.250764105
Kurtosis165.38995
Mean182328.36
Median Absolute Deviation (MAD)32607.5
Skewness10.70734698
Sum2468725994
Variance3.513009809e+11
2020-08-02T18:30:53.308166image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
40170.1%
 
10096< 0.1%
 
7466< 0.1%
 
2935< 0.1%
 
4165< 0.1%
 
3055< 0.1%
 
1095< 0.1%
 
3965< 0.1%
 
7385< 0.1%
 
7314< 0.1%
 
Other values (12283)1348799.6%
 
ValueCountFrequency (%) 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
51< 0.1%
 
72< 0.1%
 
ValueCountFrequency (%) 
156321311< 0.1%
 
138231081< 0.1%
 
115181701< 0.1%
 
110225491< 0.1%
 
107498961< 0.1%
 

notional_imbalance
Real number (ℝ≥0)

Distinct count13534
Unique (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8964253.692895126
Minimum43.06
Maximum726894091.5
Zeros0
Zeros (%)0.0%
Memory size105.8 KiB
2020-08-02T18:30:53.422236image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum43.06
5-th percentile18173.623
Q1261832.435
median1755731.565
Q37296921.545
95-th percentile37703190.83
Maximum726894091.5
Range726894048.4
Interquartile range (IQR)7035089.11

Descriptive statistics

Standard deviation26656185.84
Coefficient of variation (CV)2.973609043
Kurtosis171.769173
Mean8964253.693
Median Absolute Deviation (MAD)1699118.275
Skewness10.45264749
Sum1.21375995e+11
Variance7.105522437e+14
2020-08-02T18:30:53.526956image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
52912< 0.1%
 
419.192< 0.1%
 
178123.322< 0.1%
 
2851.122< 0.1%
 
2704.382< 0.1%
 
85735.22< 0.1%
 
1200950.41< 0.1%
 
3140717.581< 0.1%
 
1038033.661< 0.1%
 
82161641< 0.1%
 
Other values (13524)1352499.9%
 
ValueCountFrequency (%) 
43.061< 0.1%
 
117.181< 0.1%
 
160.31< 0.1%
 
1621< 0.1%
 
170.141< 0.1%
 
ValueCountFrequency (%) 
726894091.51< 0.1%
 
630726116.41< 0.1%
 
616054366.41< 0.1%
 
611672417.91< 0.1%
 
531432864.21< 0.1%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size105.8 KiB
0
10086
1
3454
ValueCountFrequency (%) 
01008674.5%
 
1345425.5%
 

matchedsize
Real number (ℝ≥0)

Distinct count13345
Unique (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean770950.6694239291
Minimum40
Maximum56634729
Zeros0
Zeros (%)0.0%
Memory size105.8 KiB
2020-08-02T18:30:53.644034image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile6440.75
Q156526.25
median217862
Q3645430.25
95-th percentile3188998.85
Maximum56634729
Range56634689
Interquartile range (IQR)588904

Descriptive statistics

Standard deviation2051935.686
Coefficient of variation (CV)2.661565477
Kurtosis154.472265
Mean770950.6694
Median Absolute Deviation (MAD)193498
Skewness9.639695021
Sum1.043867206e+10
Variance4.210440059e+12
2020-08-02T18:30:53.768628image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4706093< 0.1%
 
25943< 0.1%
 
805503< 0.1%
 
33833< 0.1%
 
54993< 0.1%
 
106663< 0.1%
 
2003< 0.1%
 
45793< 0.1%
 
247053< 0.1%
 
107262< 0.1%
 
Other values (13335)1351199.8%
 
ValueCountFrequency (%) 
402< 0.1%
 
1002< 0.1%
 
1081< 0.1%
 
1401< 0.1%
 
1831< 0.1%
 
ValueCountFrequency (%) 
566347291< 0.1%
 
464895011< 0.1%
 
461553381< 0.1%
 
447786101< 0.1%
 
435614521< 0.1%
 

currentreferenceprice
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count9549
Unique (%)70.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.67645516986707
Minimum1.425
Maximum1226.49
Zeros0
Zeros (%)0.0%
Memory size105.8 KiB
2020-08-02T18:30:53.905086image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1.425
5-th percentile7.51925
Q125.125
median49.71
Q391.475
95-th percentile216.3685
Maximum1226.49
Range1225.065
Interquartile range (IQR)66.35

Descriptive statistics

Standard deviation88.54756676
Coefficient of variation (CV)1.185749465
Kurtosis39.78450714
Mean74.67645517
Median Absolute Deviation (MAD)29.93
Skewness4.789966878
Sum1011119.203
Variance7840.67158
2020-08-02T18:30:54.073986image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
52.7370.1%
 
57.970.1%
 
30.316< 0.1%
 
40.896< 0.1%
 
7.3456< 0.1%
 
36.746< 0.1%
 
16.96< 0.1%
 
186< 0.1%
 
49.666< 0.1%
 
38.36< 0.1%
 
Other values (9539)1347899.5%
 
ValueCountFrequency (%) 
1.4252< 0.1%
 
1.4751< 0.1%
 
1.481< 0.1%
 
1.532< 0.1%
 
1.7751< 0.1%
 
ValueCountFrequency (%) 
1226.492< 0.1%
 
1178.431< 0.1%
 
1177.981< 0.1%
 
1163.891< 0.1%
 
1162.571< 0.1%
 

nearindicativeprice
Real number (ℝ≥0)

ZEROS

Distinct count6924
Unique (%)51.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.68568094534713
Minimum0.0
Maximum9999.99
Zeros3622
Zeros (%)26.8%
Memory size105.8 KiB
2020-08-02T18:30:54.223636image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median31.79
Q374.0325
95-th percentile209.933
Maximum9999.99
Range9999.99
Interquartile range (IQR)74.0325

Descriptive statistics

Standard deviation427.8036695
Coefficient of variation (CV)4.47092674
Kurtosis106.0789199
Mean95.68568095
Median Absolute Deviation (MAD)31.79
Skewness9.887576282
Sum1295584.12
Variance183015.9797
2020-08-02T18:30:54.329274image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0362226.8%
 
4294.671280.9%
 
0.01990.7%
 
22100.1%
 
3890.1%
 
4590.1%
 
1680.1%
 
9080.1%
 
4280.1%
 
11070.1%
 
Other values (6914)963271.1%
 
ValueCountFrequency (%) 
0362226.8%
 
0.01990.7%
 
0.056< 0.1%
 
1.41< 0.1%
 
1.482< 0.1%
 
ValueCountFrequency (%) 
9999.991< 0.1%
 
4294.671280.9%
 
1257.761< 0.1%
 
1255.051< 0.1%
 
1191.71< 0.1%
 

farindicativeprice
Real number (ℝ≥0)

ZEROS

Distinct count3802
Unique (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.90169719350074
Minimum0.0
Maximum1259.72
Zeros7729
Zeros (%)57.1%
Memory size105.8 KiB
2020-08-02T18:30:54.454923image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q344.57
95-th percentile150.2425
Maximum1259.72
Range1259.72
Interquartile range (IQR)44.57

Descriptive statistics

Standard deviation73.94421135
Coefficient of variation (CV)2.181135975
Kurtosis64.75311816
Mean33.90169719
Median Absolute Deviation (MAD)0
Skewness6.098403884
Sum459028.98
Variance5467.746392
2020-08-02T18:30:54.620617image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0772957.1%
 
31.170.1%
 
3.86< 0.1%
 
16.195< 0.1%
 
53.765< 0.1%
 
10.355< 0.1%
 
48.115< 0.1%
 
10.785< 0.1%
 
54.835< 0.1%
 
35.755< 0.1%
 
Other values (3792)576342.6%
 
ValueCountFrequency (%) 
0772957.1%
 
1.512< 0.1%
 
1.812< 0.1%
 
2.022< 0.1%
 
2.062< 0.1%
 
ValueCountFrequency (%) 
1259.722< 0.1%
 
1179.582< 0.1%
 
1159.092< 0.1%
 
1151.021< 0.1%
 
1093.472< 0.1%
 

matched_to_unmatched
Real number (ℝ≥0)

SKEWED

Distinct count13521
Unique (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6145759948403059
Minimum2.168374261e-05
Maximum246.6423638
Zeros0
Zeros (%)0.0%
Memory size105.8 KiB
2020-08-02T18:30:54.757643image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum2.168374261e-05
5-th percentile0.007668501358
Q10.05069985953
median0.1775108101
Q30.4747253415
95-th percentile1.960591043
Maximum246.6423638
Range246.6423421
Interquartile range (IQR)0.424025482

Descriptive statistics

Standard deviation3.547471381
Coefficient of variation (CV)5.772225747
Kurtosis2623.700714
Mean0.6145759948
Median Absolute Deviation (MAD)0.1496609123
Skewness44.00783743
Sum8321.35897
Variance12.5845532
2020-08-02T18:30:54.858823image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.035381261012< 0.1%
 
6.7392120082< 0.1%
 
1.184905662< 0.1%
 
1.2814814812< 0.1%
 
1.4806258762< 0.1%
 
0.10991809352< 0.1%
 
6.9252< 0.1%
 
2.1528718062< 0.1%
 
1.6918457652< 0.1%
 
0.37068771142< 0.1%
 
Other values (13511)1352099.9%
 
ValueCountFrequency (%) 
2.168374261e-051< 0.1%
 
4.702539371e-051< 0.1%
 
5.804308669e-051< 0.1%
 
7.905388313e-051< 0.1%
 
0.00011687314261< 0.1%
 
ValueCountFrequency (%) 
246.64236381< 0.1%
 
204.79252581< 0.1%
 
99.21093751< 0.1%
 
87.534344341< 0.1%
 
76.119444081< 0.1%
 

pct_move_snapshot_to_close
Real number (ℝ)

SKEWED
ZEROS

Distinct count380
Unique (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.08700147710487445
Minimum-76.22
Maximum6.05
Zeros532
Zeros (%)3.9%
Memory size105.8 KiB
2020-08-02T18:30:54.971384image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-76.22
5-th percentile-0.6
Q1-0.22
median-0.04
Q30.13
95-th percentile0.52
Maximum6.05
Range82.27
Interquartile range (IQR)0.35

Descriptive statistics

Standard deviation1.857441287
Coefficient of variation (CV)-21.34953737
Kurtosis1546.90537
Mean-0.0870014771
Median Absolute Deviation (MAD)0.18
Skewness-38.47594513
Sum-1178
Variance3.450088135
2020-08-02T18:30:55.078488image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
05323.9%
 
-0.052361.7%
 
-0.062241.7%
 
-0.112201.6%
 
-0.022111.6%
 
0.032101.6%
 
0.072101.6%
 
-0.042091.5%
 
-0.032061.5%
 
-0.082041.5%
 
Other values (370)1107881.8%
 
ValueCountFrequency (%) 
-76.221< 0.1%
 
-76.181< 0.1%
 
-76.111< 0.1%
 
-76.091< 0.1%
 
-741< 0.1%
 
ValueCountFrequency (%) 
6.051< 0.1%
 
5.641< 0.1%
 
2.961< 0.1%
 
2.91< 0.1%
 
2.761< 0.1%
 

day
Real number (ℝ≥0)

Distinct count5
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.86019202363368
Minimum19
Maximum29
Zeros0
Zeros (%)0.0%
Memory size105.8 KiB
2020-08-02T18:30:55.190917image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile19
Q120
median26
Q327
95-th percentile29
Maximum29
Range10
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.7651959
Coefficient of variation (CV)0.157802414
Kurtosis-1.681960878
Mean23.86019202
Median Absolute Deviation (MAD)3
Skewness-0.1152296116
Sum323067
Variance14.17670016
2020-08-02T18:30:55.685896image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
26388728.7%
 
20382228.2%
 
19197514.6%
 
29196414.5%
 
27189214.0%
 
ValueCountFrequency (%) 
19197514.6%
 
20382228.2%
 
26388728.7%
 
27189214.0%
 
29196414.5%
 
ValueCountFrequency (%) 
29196414.5%
 
27189214.0%
 
26388728.7%
 
20382228.2%
 
19197514.6%
 

quarter
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size105.8 KiB
2
5914
4
3840
3
3786
ValueCountFrequency (%) 
2591443.7%
 
4384028.4%
 
3378628.0%
 
2020-08-02T18:30:55.885792image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

year
Categorical

HIGH CORRELATION

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size105.8 KiB
2019
7626
2020
5914
ValueCountFrequency (%) 
2019762656.3%
 
2020591443.7%
 
2020-08-02T18:30:56.109445image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

week
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count7
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.87658788774003
Minimum22
Maximum51
Zeros0
Zeros (%)0.0%
Memory size105.8 KiB
2020-08-02T18:30:56.244518image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile22
Q125
median35
Q348
95-th percentile51
Maximum51
Range29
Interquartile range (IQR)23

Descriptive statistics

Standard deviation10.605868
Coefficient of variation (CV)0.3040970645
Kurtosis-1.410765382
Mean34.87658789
Median Absolute Deviation (MAD)10
Skewness0.3085576813
Sum472229
Variance112.484436
2020-08-02T18:30:56.370689image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
26197514.6%
 
25197514.6%
 
22196414.5%
 
51192814.2%
 
48191214.1%
 
38189414.0%
 
35189214.0%
 
ValueCountFrequency (%) 
22196414.5%
 
25197514.6%
 
26197514.6%
 
35189214.0%
 
38189414.0%
 
ValueCountFrequency (%) 
51192814.2%
 
48191214.1%
 
38189414.0%
 
35189214.0%
 
26197514.6%
 

month
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count6
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.114475627769572
Minimum5
Maximum12
Zeros0
Zeros (%)0.0%
Memory size105.8 KiB
2020-08-02T18:30:56.487013image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q16
median8
Q311
95-th percentile12
Maximum12
Range7
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.479676796
Coefficient of variation (CV)0.3055868192
Kurtosis-1.364953584
Mean8.114475628
Median Absolute Deviation (MAD)2
Skewness0.3084892935
Sum109870
Variance6.148797014
2020-08-02T18:30:56.651292image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
6395029.2%
 
5196414.5%
 
12192814.2%
 
11191214.1%
 
9189414.0%
 
8189214.0%
 
ValueCountFrequency (%) 
5196414.5%
 
6395029.2%
 
8189214.0%
 
9189414.0%
 
11191214.1%
 
ValueCountFrequency (%) 
12192814.2%
 
11191214.1%
 
9189414.0%
 
8189214.0%
 
6395029.2%
 

Interactions

2020-08-02T18:30:12.420088image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:12.588023image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:12.729241image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:13.485401image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:13.615784image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:13.734570image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:13.867801image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:14.003312image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:14.143073image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:14.273017image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:14.394088image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:14.509904image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:14.630421image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:14.749002image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:14.870304image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:14.987653image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:15.111205image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:15.251204image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:15.371141image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:15.477640image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:15.585195image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:15.699424image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:15.809318image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:15.924675image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:16.047055image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:16.161392image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:16.274233image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:16.402979image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:16.519674image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:16.640873image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:16.751456image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:16.854226image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:16.957358image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:17.066673image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-08-02T18:30:17.517182image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-08-02T18:30:17.854394image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-08-02T18:30:18.847423image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:18.962487image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:19.072653image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:19.182980image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-08-02T18:30:19.433135image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:19.555425image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:19.673519image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:19.807336image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:19.938554image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:20.060146image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:20.200691image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:20.335951image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:20.453231image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:20.580366image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:20.712535image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:20.839773image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:20.952891image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:21.066813image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:21.180673image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:21.309152image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:21.446342image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:21.567207image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:21.676620image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:21.809773image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:21.943204image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:22.074926image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:22.185680image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:22.305591image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:22.439075image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:22.568578image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:22.693742image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:22.828376image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:22.963143image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:23.096568image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:23.231065image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:23.359032image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:23.478488image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:23.608068image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:23.737978image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:23.854445image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:24.002051image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-08-02T18:30:24.264860image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-08-02T18:30:24.535429image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-08-02T18:30:24.973835image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:25.110194image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:25.229778image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:25.361759image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-08-02T18:30:25.612148image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:25.732638image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:25.859540image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-08-02T18:30:26.593129image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:26.727899image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:26.858614image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:26.999789image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:27.147034image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:27.285047image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-08-02T18:30:27.536267image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-08-02T18:30:27.824052image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:27.960292image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-08-02T18:30:28.218654image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:28.359435image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:28.502996image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:28.622103image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:28.785002image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:28.955790image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-02T18:30:29.129582image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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Correlations

2020-08-02T18:30:56.868786image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-02T18:30:57.266001image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-02T18:30:57.685799image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-02T18:30:58.088849image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-08-02T18:30:58.405148image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

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2020-08-02T18:30:51.618259image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

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11018082809215.350215.34215.361215714.645315e+060401702215.34215.000.000.0536990.172622020266
22014197429220.330220.32220.331195684.311417e+0602029174220.33220.01220.010.0096430.141922020256
33014604707220.310220.30220.3301386853.055369e+0702128222220.33221.670.000.0651650.151922020256
44025792889205.500205.49205.50023396304.807940e+0816173408205.490.000.000.3789850.922922020225
55027551034206.468206.44206.48015295473.158025e+0808758311206.46215.600.000.1746390.452922020225
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88019348988193.600193.57193.60031820996.160544e+08113615349193.580.000.000.2337140.5726420194811
99020898025193.750193.74193.77016881193.270731e+08017121808193.77194.55195.370.0985950.4926420194811

Last rows

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